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Found 7 papers, 2 papers with code
Global Convergence of Model Function Based Bregman Proximal Minimization Algorithms
We fix this issue by proposing the MAP property, which generalizes the $L$-smad property and is also valid for a large class of nonconvex nonsmooth composite problems.
Bregman Proximal Framework for Deep Linear Neural Networks
This initiated the development of the Bregman proximal gradient (BPG) algorithm and an inertial variant (momentum based) CoCaIn BPG, which however rely on problem dependent Bregman distances.
Beyond Alternating Updates for Matrix Factorization with Inertial Bregman Proximal Gradient Algorithms
Matrix Factorization is a popular non-convex optimization problem, for which alternating minimization schemes are mostly used.
Convex-Concave Backtracking for Inertial Bregman Proximal Gradient Algorithms in Non-Convex Optimization
Backtracking line-search is an old yet powerful strategy for finding a better step sizes to be used in proximal gradient algorithms.
On the loss landscape of a class of deep neural networks with no bad local valleys
We identify a class of over-parameterized deep neural networks with standard activation functions and cross-entropy loss which provably have no bad local valley, in the sense that from any point in parameter space there exists a continuous path on which the cross-entropy loss is non-increasing and gets arbitrarily close to zero.
Neural Networks Should Be Wide Enough to Learn Disconnected Decision Regions
In the recent literature the important role of depth in deep learning has been emphasized.
Variants of RMSProp and Adagrad with Logarithmic Regret Bounds
Adaptive gradient methods have become recently very popular, in particular as they have been shown to be useful in the training of deep neural networks.
